{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9a002bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple # 使用具名元组\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler,LabelEncoder\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52c25735",
   "metadata": {},
   "outputs": [],
   "source": [
    "rnames = ['userId','movieId','rating','userGenre1','userGenre2','userGenre3',\n",
    "                                                        'movieGenre1','movieGenre2','movieGenre3','releaseYear','movieRatingCount','movieAvgRating',\n",
    "                                                       'movieRatingStddev','userAvgRating','userRatingStddev']\n",
    "data = pd.read_csv(r\"data/trainingSamples.csv\",usecols=['userId','movieId','rating','userGenre1','userGenre2','userGenre3',\n",
    "                                                        'movieGenre1','movieGenre2','movieGenre3','releaseYear','movieRatingCount','movieAvgRating',\n",
    "                                                       'movieRatingStddev','userAvgRating','userRatingStddev'])\n",
    "# userRatingCount\n",
    "# data.columns=rnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1b1d109",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "      <th>releaseYear</th>\n",
       "      <th>movieGenre1</th>\n",
       "      <th>movieGenre2</th>\n",
       "      <th>movieGenre3</th>\n",
       "      <th>movieRatingCount</th>\n",
       "      <th>movieAvgRating</th>\n",
       "      <th>movieRatingStddev</th>\n",
       "      <th>userAvgRating</th>\n",
       "      <th>userRatingStddev</th>\n",
       "      <th>userGenre1</th>\n",
       "      <th>userGenre2</th>\n",
       "      <th>userGenre3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Thriller</td>\n",
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       "    <tr>\n",
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       "      <td>1995</td>\n",
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       "      <td>Action</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>17686</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>6330</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>20158</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3954</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "      <td>1824</td>\n",
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       "      <td>1.06</td>\n",
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       "      <td>0.85</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88823</th>\n",
       "      <td>968</td>\n",
       "      <td>8507</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1968</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Sci-Fi</td>\n",
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       "      <td>3.64</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88824</th>\n",
       "      <td>969</td>\n",
       "      <td>16689</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>2380</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Crime</td>\n",
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       "    <tr>\n",
       "      <th>88825</th>\n",
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       "      <td>26460</td>\n",
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       "      <td>1951</td>\n",
       "      <td>Adventure</td>\n",
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       "      <td>2380</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.42</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Drama</td>\n",
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       "    <tr>\n",
       "      <th>88826</th>\n",
       "      <td>970</td>\n",
       "      <td>3033</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1953</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Crime</td>\n",
       "      <td>98</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.89</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88827 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId  userId  rating  releaseYear movieGenre1 movieGenre2  \\\n",
       "0            1   15555     3.0         1995   Adventure   Animation   \n",
       "1            1   25912     3.5         1995   Adventure   Animation   \n",
       "2            1   29912     3.0         1995   Adventure   Animation   \n",
       "3           10   17686     0.5         1995      Action   Adventure   \n",
       "4          104   20158     4.0         1996      Comedy         NaN   \n",
       "...        ...     ...     ...          ...         ...         ...   \n",
       "88822      968   26865     3.0         1968      Horror      Sci-Fi   \n",
       "88823      968    8507     2.0         1968      Horror      Sci-Fi   \n",
       "88824      969   16689     5.0         1951   Adventure      Comedy   \n",
       "88825      969   26460     2.0         1951   Adventure      Comedy   \n",
       "88826      970    3033     2.0         1953   Adventure      Comedy   \n",
       "\n",
       "      movieGenre3  movieRatingCount  movieAvgRating  movieRatingStddev  \\\n",
       "0        Children             10759            3.91               0.89   \n",
       "1        Children             10759            3.91               0.89   \n",
       "2        Children             10759            3.91               0.89   \n",
       "3        Thriller              6330            3.42               0.87   \n",
       "4             NaN              3954            3.40               1.06   \n",
       "...           ...               ...             ...                ...   \n",
       "88822    Thriller              1824            3.64               1.06   \n",
       "88823    Thriller              1824            3.64               1.06   \n",
       "88824     Romance              2380            4.11               0.86   \n",
       "88825     Romance              2380            4.11               0.86   \n",
       "88826       Crime                98            3.51               0.96   \n",
       "\n",
       "       userAvgRating  userRatingStddev userGenre1 userGenre2 userGenre3  \n",
       "0               3.86              0.74      Drama     Comedy   Thriller  \n",
       "1               3.48              1.28     Action     Comedy    Romance  \n",
       "2               3.00              0.00        NaN        NaN        NaN  \n",
       "3               2.97              1.48     Comedy      Drama  Adventure  \n",
       "4               3.60              0.72   Thriller      Drama     Action  \n",
       "...              ...               ...        ...        ...        ...  \n",
       "88822           3.35              0.85      Drama   Thriller     Comedy  \n",
       "88823           2.00              1.00        NaN        NaN        NaN  \n",
       "88824           3.53              0.82      Drama     Comedy      Crime  \n",
       "88825           2.73              1.42   Thriller      Crime      Drama  \n",
       "88826           3.67              0.89      Drama    Romance     Comedy  \n",
       "\n",
       "[88827 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "975768cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_features = ['releaseYear','movieRatingCount','movieAvgRating','movieRatingStddev','userAvgRating','userRatingStddev']\n",
    "sparse_features = ['userId','movieId','userGenre1','userGenre2','userGenre3','movieGenre1','movieGenre2','movieGenre3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd6fbca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"数据处理\"\"\"\n",
    "def data_process(data, dense_features, sparse_features):\n",
    "    # 将数值特征的空值位置填补为0\n",
    "    data[dense_features] = data[dense_features].fillna(0.0)\n",
    "    # 调整分布\n",
    "    for f in dense_features:\n",
    "        print(f)\n",
    "        data[f] = data[f].apply(lambda x: np.log(x+1) if int(x) > -1 else -1)\n",
    "    \n",
    "    # 将分类特征进行编码，由于原数据中的类别都是字符串，所以要使用LabelEncoder编码成数值\n",
    "    data[sparse_features]=data[sparse_features].fillna(\"0\") # 将类别特征进行填补，使用字符串\n",
    "    \n",
    "    for f in sparse_features:\n",
    "        le = LabelEncoder()\n",
    "        data[f]=le.fit_transform(data[f])\n",
    "    \n",
    "    return data[dense_features + sparse_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59db1355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "releaseYear\n",
      "movieRatingCount\n",
      "movieAvgRating\n",
      "movieRatingStddev\n",
      "userAvgRating\n",
      "userRatingStddev\n"
     ]
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       "    <tr>\n",
       "      <th>88823</th>\n",
       "      <td>7.585281</td>\n",
       "      <td>7.509335</td>\n",
       "      <td>1.534714</td>\n",
       "      <td>0.722706</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6048</td>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88824</th>\n",
       "      <td>7.576610</td>\n",
       "      <td>7.775276</td>\n",
       "      <td>1.631199</td>\n",
       "      <td>0.620576</td>\n",
       "      <td>1.510722</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>11905</td>\n",
       "      <td>895</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88825</th>\n",
       "      <td>7.576610</td>\n",
       "      <td>7.775276</td>\n",
       "      <td>1.631199</td>\n",
       "      <td>0.620576</td>\n",
       "      <td>1.316408</td>\n",
       "      <td>0.883768</td>\n",
       "      <td>18796</td>\n",
       "      <td>895</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88826</th>\n",
       "      <td>7.577634</td>\n",
       "      <td>4.595120</td>\n",
       "      <td>1.506297</td>\n",
       "      <td>0.672944</td>\n",
       "      <td>1.541159</td>\n",
       "      <td>0.636577</td>\n",
       "      <td>2192</td>\n",
       "      <td>896</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88827 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       releaseYear  movieRatingCount  movieAvgRating  movieRatingStddev  \\\n",
       "0         7.598900          9.283591        1.591274           0.636577   \n",
       "1         7.598900          9.283591        1.591274           0.636577   \n",
       "2         7.598900          9.283591        1.591274           0.636577   \n",
       "3         7.598900          8.753213        1.486140           0.625938   \n",
       "4         7.599401          8.282736        1.481605           0.722706   \n",
       "...            ...               ...             ...                ...   \n",
       "88822     7.585281          7.509335        1.534714           0.722706   \n",
       "88823     7.585281          7.509335        1.534714           0.722706   \n",
       "88824     7.576610          7.775276        1.631199           0.620576   \n",
       "88825     7.576610          7.775276        1.631199           0.620576   \n",
       "88826     7.577634          4.595120        1.506297           0.672944   \n",
       "\n",
       "       userAvgRating  userRatingStddev  userId  movieId  userGenre1  \\\n",
       "0           1.581038          0.553885   11096        0           8   \n",
       "1           1.499623          0.824175   18410        0           1   \n",
       "2           1.386294          0.000000   21222        0           0   \n",
       "3           1.378766          0.908259   12598        9           5   \n",
       "4           1.526056          0.542324   14326      101          17   \n",
       "...              ...               ...     ...      ...         ...   \n",
       "88822       1.470176          0.615186   19066      894           8   \n",
       "88823       1.098612          0.693147    6048      894           0   \n",
       "88824       1.510722          0.598837   11905      895           8   \n",
       "88825       1.316408          0.883768   18796      895          17   \n",
       "88826       1.541159          0.636577    2192      896           8   \n",
       "\n",
       "       userGenre2  userGenre3  movieGenre1  movieGenre2  movieGenre3  label  \n",
       "0               5          17            1            2            2    3.0  \n",
       "1               5          15            1            2            2    3.5  \n",
       "2               0           0            1            2            2    3.0  \n",
       "3               8           2            0            1           13    0.5  \n",
       "4               8           1            4            0            0    4.0  \n",
       "...           ...         ...          ...          ...          ...    ...  \n",
       "88822          17           5           10           15           13    3.0  \n",
       "88823           0           0           10           15           13    2.0  \n",
       "88824           5           6            1            4           11    5.0  \n",
       "88825           6           8            1            4           11    2.0  \n",
       "88826          15           5            1            4            4    2.0  \n",
       "\n",
       "[88827 rows x 15 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = data_process(data, dense_features, sparse_features)\n",
    "train_data['label'] = data['rating']\n",
    "train_data # (200,40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cb58f7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SparseFeat(name='userId', vocabulary_size=21290, embedding_dim=4),\n",
       " SparseFeat(name='movieId', vocabulary_size=922, embedding_dim=4),\n",
       " SparseFeat(name='userGenre1', vocabulary_size=20, embedding_dim=4),\n",
       " SparseFeat(name='userGenre2', vocabulary_size=20, embedding_dim=4),\n",
       " SparseFeat(name='userGenre3', vocabulary_size=20, embedding_dim=4),\n",
       " SparseFeat(name='movieGenre1', vocabulary_size=18, embedding_dim=4),\n",
       " SparseFeat(name='movieGenre2', vocabulary_size=19, embedding_dim=4),\n",
       " SparseFeat(name='movieGenre3', vocabulary_size=16, embedding_dim=4),\n",
       " DenseFeat(name='releaseYear', dimension=1),\n",
       " DenseFeat(name='movieRatingCount', dimension=1),\n",
       " DenseFeat(name='movieAvgRating', dimension=1),\n",
       " DenseFeat(name='movieRatingStddev', dimension=1),\n",
       " DenseFeat(name='userAvgRating', dimension=1),\n",
       " DenseFeat(name='userRatingStddev', dimension=1)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"使用具名元组为特征做标记\"\"\"\n",
    "SparseFeat = namedtuple('SparseFeat', ['name', 'vocabulary_size', 'embedding_dim'])\n",
    "DenseFeat = namedtuple('DenseFeat', ['name', 'dimension'])\n",
    "\n",
    "dnn_features_columns = [SparseFeat(name=feat, vocabulary_size=data[feat].nunique(), embedding_dim = 4) for feat in sparse_features] + [DenseFeat(name=feat, dimension=1) for feat in dense_features]\n",
    "dnn_features_columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d0f5a313",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"构建输入层\"\"\"\n",
    "def build_input_layers(dnn_features_columns):\n",
    "    dense_input_dict, sparse_input_dict = {}, {}\n",
    "    \n",
    "    for f in dnn_features_columns:\n",
    "        if isinstance(f, SparseFeat):\n",
    "            sparse_input_dict[f.name] = Input(shape=(1, ), name=f.name)\n",
    "        elif isinstance(f, DenseFeat):\n",
    "            dense_input_dict[f.name] = Input(shape=(f.dimension, ), name=f.name)\n",
    "    \n",
    "    return dense_input_dict, sparse_input_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01a364cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"将类别特征进行embedding\"\"\"\n",
    "def build_embedding_layers(dnn_features_columns, input_layers_dict, is_linear):\n",
    "    embedding_layer_dict = {}\n",
    "    \n",
    "    # 将sparse特征筛选出来\n",
    "    sparse_feature_columns = list(filter(lambda x: isinstance(x,SparseFeat), dnn_features_columns)) if dnn_features_columns else []\n",
    "    \n",
    "    # 如果是用于线性部分的embedding层，其维度为1，否则维度就是自己定义的embedding维度\n",
    "    if is_linear:\n",
    "        for f in sparse_feature_columns:\n",
    "            embedding_layer_dict[f.name] = Embedding(f.vocabulary_size + 1, 1, name='1d_emb_' + f.name)\n",
    "    \n",
    "    else:\n",
    "        for f in sparse_feature_columns:\n",
    "            embedding_layer_dict[f.name] = Embedding(f.vocabulary_size + 1, f.embedding_dim, name='kd_emb_' + f.name)\n",
    "    \n",
    "    return embedding_layer_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "525831f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"将所有的sparse特征embedding进行拼接\"\"\"\n",
    "def concat_embedding_list(dnn_features_columns, input_layer_dict, embedding_layer_dict, flatten=False):\n",
    "    # 筛选sparse特征\n",
    "    sparse_feature_columns = list(filter(lambda x: isinstance(x, SparseFeat), dnn_features_columns))\n",
    "    \n",
    "    embedding_list = []\n",
    "    for f in sparse_feature_columns:\n",
    "        _input = input_layer_dict[f.name]\n",
    "        _embed = embedding_layer_dict[f.name]\n",
    "        embed = _embed(_input)\n",
    "        \n",
    "        if flatten:\n",
    "            embed = Flatten()(embed)\n",
    "        \n",
    "        embedding_list.append(embed)\n",
    "    \n",
    "    return embedding_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2fe0f709",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"构建残差块\"\"\"\n",
    "class ResidualBlock(Layer):\n",
    "    def __init__(self, units):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.units = units\n",
    "    \n",
    "    def build(self, input_shape):\n",
    "        out_dim = input_shape[-1]\n",
    "        self.dnn1 = Dense(self.units, activation='relu')\n",
    "        self.dnn2 = Dense(out_dim, activation='relu')\n",
    "    \n",
    "    def call(self, inputs):\n",
    "        x = inputs\n",
    "        x = self.dnn1(x)\n",
    "        x = self.dnn2(x)\n",
    "        x = Activation('relu')(x + inputs)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05cb9dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"构建输出层\"\"\"\n",
    "def get_dnn_logits(dnn_inputs, block_nums=3):\n",
    "    dnn_out = dnn_inputs\n",
    "    \n",
    "    for i in range(block_nums):\n",
    "        dnn_out = ResidualBlock(64)(dnn_out)\n",
    "    \n",
    "    dnn_logits = Dense(1, activation='sigmoid')(dnn_out)\n",
    "\n",
    "    return dnn_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c2be5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"构建模型\"\"\"\n",
    "def DeepCrossing(dnn_features_columns):\n",
    "    # 1.构建输入层\n",
    "    dense_input_dic, sparse_input_dic = build_input_layers(dnn_features_columns)\n",
    "    input_layers = list(dense_input_dic.values()) + list(sparse_input_dic.values())\n",
    "    \n",
    "    # 2.将类别特征进行embedding\n",
    "    embedding_layer_dict = build_embedding_layers(dnn_features_columns, sparse_input_dic, is_linear=False)\n",
    "    \n",
    "    # 3.将数值型特征拼接在一起\n",
    "    dense_dnn_list = list(dense_input_dic.values())\n",
    "    dense_dnn_inputs = Concatenate(axis=1)(dense_dnn_list)\n",
    "    \n",
    "    # 4.将类别Embedding向量进行Flatten\n",
    "    sparse_dnn_list = concat_embedding_list(dnn_features_columns, sparse_input_dic, embedding_layer_dict, flatten=True)\n",
    "    sparse_dnn_inputs = Concatenate(axis=1)(sparse_dnn_list)\n",
    "    \n",
    "    # 6.将数值特征和类别特征进行拼接\n",
    "    dnn_inputs = Concatenate(axis=1)([dense_dnn_inputs, sparse_dnn_inputs])\n",
    "    \n",
    "    # 7.将所有特征输入到残差模块中\n",
    "    output_layer = get_dnn_logits(dnn_inputs, block_nums=3)\n",
    "    \n",
    "    # 8.构建模型\n",
    "    model = Model(input_layers, output_layer)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5604df5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepCrossing = DeepCrossing(dnn_features_columns)\n",
    "# history.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "13fdd51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepCrossing.compile(optimizer='adam',\n",
    "               loss='binary_crossentropy',\n",
    "               metrics=['binary_crossentropy', tf.keras.metrics.AUC(name='auc')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d9e40c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_model_input = {name: data[name] for name in dense_features + sparse_features}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd042d55",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1111/1111 [==============================] - 6s 3ms/step - loss: -37901534494720.0000 - binary_crossentropy: -37901534494720.0000 - auc: 0.0000e+00 - val_loss: -350855375093760.0000 - val_binary_crossentropy: -350855375093760.0000 - val_auc: 0.0000e+00\n",
      "Epoch 2/5\n",
      "1111/1111 [==============================] - 3s 3ms/step - loss: -15230051742121984.0000 - binary_crossentropy: -15230051742121984.0000 - auc: 0.0000e+00 - val_loss: -62742875124793344.0000 - val_binary_crossentropy: -62742875124793344.0000 - val_auc: 0.0000e+00\n",
      "Epoch 3/5\n",
      "1111/1111 [==============================] - 3s 3ms/step - loss: -360287351714349056.0000 - binary_crossentropy: -360287351714349056.0000 - auc: 0.0000e+00 - val_loss: -958160886810279936.0000 - val_binary_crossentropy: -958160886810279936.0000 - val_auc: 0.0000e+00\n",
      "Epoch 4/5\n",
      "1111/1111 [==============================] - 4s 3ms/step - loss: -2848436329480454144.0000 - binary_crossentropy: -2848436329480454144.0000 - auc: 0.0000e+00 - val_loss: -5914218070225715200.0000 - val_binary_crossentropy: -5914218070225715200.0000 - val_auc: 0.0000e+00\n",
      "Epoch 5/5\n",
      "1111/1111 [==============================] - 4s 3ms/step - loss: -12953475129689505792.0000 - binary_crossentropy: -12953475129689505792.0000 - auc: 0.0000e+00 - val_loss: -23101814621945724928.0000 - val_binary_crossentropy: -23101814621945724928.0000 - val_auc: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b0f4b3e348>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deepCrossing.fit(train_model_input,\n",
    "           train_data['label'].values,\n",
    "           batch_size=64,\n",
    "           epochs=5,\n",
    "           validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1346e4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b2b082a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(deepCrossing,show_shapes=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
